Registration is useful for many reasons: for example, to compare two or more images obtained at different times, or to align images taken by sensors having different spectral responses, such as sensitivity to different coloured lights.
One type of registration combines the two images to be aligned into a composite image to obtain information as to how to better align the images. Such a composite image can be processed by considering the following: (1) the feature space (what features within the images to look for) ; (2) the search space (how one image can be `moved` relative to the other); (3) the search strategy (in what order to `move` one image relative to another to better align the images); and (4) the similarity metric (the measure of how closely the images are aligned).
Conventional registration systems often use features. By using the features in an image, the whole image does not have to be used for alignment. Instead, significant details, for example, edges of objects in the image can be extracted and aligned. The feature space defines the features which are extracted. Depending on the application, using features for registration is robust. However, registration using features requires prior knowledge about what kind of image can be expected so that the important features will be included in the feature space. In addition, if the features can not be detected reliably, special features (markers), may have to be added to the objects or to the images in order to provide reliable registration. Also, in many applications, the images to be registered are dissimilar, that is, the features are not quite the same. These dissimilarities can, for example, be caused by: changes between exposures in the depicted object; or use of different sensors to obtained images, for example, images captured from the red chip and the green chip in a 3-chip CCD (charge-coupled device) camera or images captured from sensors responding to infrared light and visible light.
The search space defines what kind of geometric transformations can be used to align the images. Examples of geometric transformations include: (1) translation (shifting); (2) rotation, and (3) relative magnification.
The search strategy is used to identify (often iteratively) the parameters of the geometric transformation required to align the images. Typically, the search strategy optimizes a similarity metric, that is, a measure of the similarity between images. If the similarity metric measures differences between images, the desired geometric transformation can be obtained by minimizing the measure. Similarly, if the similarity metric measures the sameness of images, the desired geometric transformation can be obtained by maximizing the measure. Some similarity metrics are calculated using: (1) correlation, that is, the summing of those portions from one image which are the same or similar to the corresponding portion of the other image after the portions have been normalized, weighted, statistically adjusted, or phase adjusted; (2) summing of absolute differences of: the intensity of portions of the images, contours in the images, or surfaces in the images; (3) matched filters (similar to correlation); and/or (4) summing of sign changes between portions of a difference image, that is, for example, the resulting image when one image is subtracted pixel-wise from the another image.
However, without feature extraction, only the `summing of sign changes` (sign summing) has been found to be especially useful for dissimilar images. The other similarity metrics: (1) are sensitive to the dissimilarities; (2) require features; (3) are sensitive to noise; and/or (4) are sensitive not only to dissimilarities but also to changes in illumination of the object. Some implementations of sign summing can take advantage of a characteristic of certain noise distributions in certain images wherein the number of sign changes in a pointwise intensity difference image have a maximum when the images are perfectly aligned, but such implementations require knowledge about the type of noise distribution. Also, if the noise distribution is modified (to fit within certain parameters for sign summing), the speed of the registration can be relatively slow.